Infertility and women’s well-being: The effects of female factor infertility on depression and anxiety
Bibliographic record
Abstract
Background: Female factor infertility is diagnosed when women of reproductive age are unable to conceive a pregnancy within 12 months of unprotected, frequent intercourse. Infertility affects between 11.5% and 15.7% of Canadian couples. The process of receiving a diagnosis and seeking treatment is often described as a devastating experience leading to psychological distress. Objective: A review of the literature was conducted to assess the impact of an infertility diagnosis on the development of depression and anxiety symptoms among women. The literature review was also performed to highlight a gap in the literature regarding infertility and its effects among North American women. Methods: PubMed, PsychInfo, ClinicalKey, and Google Scholar databases were searched using the MeSH terms and keywords such as, female factor infertility, infertility, mental health, depression, and anxiety. Inclusion and exclusion criteria were established to identify pertinent articles for full-text review. Results: The majority of infertility research has been conducted in Europe and Asia using self-report questionnaires. The main finding from the research was that women experienced increased levels of infertility-related depression and anxiety, particularly with longer durations of infertility, lack of spousal support, and childlessness. Conclusion: The literature demonstrated that women are at an increased risk of developing depression and anxiety following a diagnosis of infertility. Future research should focus on complementary qualitative research and quantitative research in North American populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".